用AI预测太阳耀斑是否伴随日冕物质抛射，提升空间天气预报精度。
Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network
- 融合视觉Transformer与LSTM的混合神经网络捕捉磁图时空特征。
- 在24小时内预测耀斑是否伴随日冕物质抛射，准确率显著优于传统方法。
- 揭示磁通量抵消可能触发关联型耀斑，适合空间天气研究者参考。
太阳爆发（包括耀斑和日冕物质抛射）对地球有重要影响。部分耀斑伴随日冕物质抛射，部分则不伴随，其关联性并不总是明显。本文提出一种新型深度学习方法——混合神经网络（HNN），结合视觉变换器与长短期记忆网络，分析太阳活动区线偏振磁图的时间序列数据（来自SDO/HMI）。该模型利用磁图中的时空模式，预测未来24小时内将发生的耀斑是爆发型（伴随日冕物质抛射）还是非爆发型（不伴随）。实验结果表明HNN具有优异性能。此外，结果支持磁极反转线区域的磁通量抵消可能在触发关联型耀斑中起关键作用，与已有文献一致。
原文摘要 · Abstract (English)
Solar eruptions, including flares and coronal mass ejections (CMEs), have a significant impact on Earth. Some flares are associated with CMEs, and some flares are not. The association between flares and CMEs is not always obvious. In this study, we propose a new deep learning method, specifically a hybrid neural network (HNN) that combines a vision transformer with long short-term memory, to predict associations between flares and CMEs. HNN finds spatio-temporal patterns in the time series of line-of-sight magnetograms of solar active regions (ARs) collected by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory and uses the patterns to predict whether a flare projected to occur within the next 24 hours will be eruptive (i.e., CME-associated) or confined (i.e., not CME-associated). Our experimental results demonstrate the good performance of the HNN method. Furthermore, the results show that magnetic flux cancellation in polarity inversion line regions may well play a role in triggering flare-associated CMEs, a finding consistent with literature.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。